• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.37-42
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• 2005

This paper presents an autonomous system for reconstruction of three-dimensional indoor environments using a mobile robot. The system is composed of a mobile robot, a three-dimensional scanning system, and a notebook computer for registration, observation planning and real-time three-dimensional data transferring. Three-dimensional scanning system obtains three-dimensional environmental data and performs filtering of dynamic objects. Then, it registers multiple three-dimensional scans into one coordinate system and performs observation planning which finds the next scanning position by using the layered hexahedral-map and topological-map. Then, the mobile robot moves to the next scanning position, and repeats all procedures until there is no scanning tree in topological-map. In concurrence with data scanning, three-dimensional data can be transferred through wireless-LAN in real-time. This system is experimented successfully by using a mobile robot named KARA.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.402-405
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• 2008

This paper is a proposal of Sentinel Asia for Environment (SAFE). The essential to this project is to help environmental agencies in Asia to set up an environmental monitoring system with satellite observation data. It is focused on an environmental issues originated from anthropogenic events detected as land cover and land use change in Asians' daily human life including; agriculture, global warming gas, urban environment and forest resources. It is leaded by Japan Aerospace Exploration Agency (JAXA) along with University of Tokyo and Asian Institute of Technology in Thailand under the umbrella of Sentinel Asia which is dedicated to disaster monitoring issues. It is expected to initiate a information outgoing through WWW for Asian countries to set up their national land information system focusing on environmental changes.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.805-814
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• 2012

Most hydrologic data are obtained by ground observations. New observation methods are needed for some regions to overcome difficulties in accessibility and durability of long-term observation. In 2002, NASA launched twin satellites named GRACE which were designed to measure the gravitational field of the earth. Using the GRACE monthly gravity level-2 data, we calculated terrestrial water storage change (TWSC) of the Korean peninsula in various spatial smoothing radii (0 km, 300 km, 500 km). For the validation of GRACE-based TWSC, we compared it with land-based TWSC which was obtained using the ground observation data: precipitation and evaporation from WAMIS, and runoff from GLDAS. According to the mean square-error test, GRACE-based TWSC best fits the land-based one at 500 km smoothing radius. The variation of the terrestrial water storage in the Korean peninsula turned out to be 0.986 cm/month, which means that appropriate measures should be prepared for sustainable water resources management.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.1
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• pp.83-91
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• 2021

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.37-42
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• 2003

We have analyzed the characteristics of the sea surface winds and wind waves near the Korean marginal seas on the basis of prediction results of the sea surface winds from MM5/KMA model, which is being used for the operation system at the Korea Meteorological observation buoy data to verify the model results during Typhoon events. The correlation coefficients between the models and observation data reach up to about 95％, supporting that these models satisfactorily simulate the sea surface winds and wave heights even at the coastal regions. Based on these verification results, we have carried out numerical experiments about the wave modulation. When there exist an opposite strong current for the propagation direction of the waves or wind direction, wave height and length gets higher and shorter, and vice versa. It is proved that these modulations of wave parameters are well generated when wind speed is relatively week.

• Economic and Environmental Geology
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• v.18 no.3
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• pp.217-224
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• 1985

A Householder's transformation is applied to the resistivity inversion problem. The conventional resistivity inversion method is sometimes numerically unstable in interpreting a resistivity sounding data because it usually solves the normal equation derived from an observation equation. The resistivity inversion method using Householder's transformation solves the observation equation directly, so that it is numerically more stable than the conventional method. A theoretical, ill-conditioned Schlumberger sounding data was chosen to test the inversion scheme with Householder's transformation.

• Journal of Environmental Science International
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• v.28 no.4
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• pp.423-433
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• 2019

This study was carried out to identify the problems of the underground watersheds on Jeju Island, and to establish the hydraulic groundwater basin to be used as basis for the analysis of the groundwater model. In order to evaluate the adequacy of the groundwater basin on Jeju Island, a correlation analysis between elevation and groundwater level was conducted using data from 125 observation wells. The analysis, conducted with an elevation step of 100 m, exhibited values of R2 in the range 0.1653-0.8011. No clear correlation was observed between elevation and groundwater level. In particular, the eastern and western areas showed an inverse proportionality between elevation and groundwater level. The Kriging technique was used to analyze the underground water level data and to define the equipotential lines for all areas of Jeju Island. Eight groundwater watersheds were delineated by considering the direction of groundwater flow, the positions of the observation wells, and the long and short axes of the watersheds.

• Ocean Systems Engineering
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• v.4 no.4
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• pp.309-325
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• 2014

Geotextile tubes are basically a huge sack filled with sand or dredged soil. Geotextile tubes are made of permeable woven or non-woven synthetic fibers (i.e., polyester or PET and polypropylene or PP). The geotextile tubes' performances in strength, dewatering, retaining solid particles and stacked stability have been studied extensively in the past. However, only little research has been done in the observation of the deformation behavior of geotextile tubes. In this paper, a large-scale apparatus for geotextile tube experiment is introduced. The apparatus is equipped with a slurry mixing station, pumping and delivery station, an observation station and a data station. For this study the large-scale apparatus was utilized in the studies regarding the stresses on the geotextile and the deformation behavior of the geotextile tube. Model tests were conducted using a custom-made woven geotextile tubes. Load cells placed at the inner belly of the geotextile tube to monitor the total soil pressure. Strain gauges were also placed on the outer skin of the tube to measure the geotextile strain. The pressure and strain sensors are attached to a data logger that sends the collected data to a desktop computer. The experiment results showed that the maximum geotextile strain occurs at the sides of the tube and the soil pressure distribution varies at each geotextile tube section.

• Journal of Soil and Groundwater Environment
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• v.15 no.6
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• pp.46-52
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• 2010

Derivative analysis, based on the derivative of the drawdown as a function of time (i.e., rate of drawdown change), was applied to the evaluation of hydraulic parameters of the aquifer as an aid of the aquifer test interpretation based on the Theis solutions. Pumping tests were conducted at a coastal fractured aquifer in Muan county, Korea, of which the drawdown data, measured at the two observation wells, were used for derivative analysis. Wellbore storage and transition period were hard to identify at conventional log-log and semi long plots, but was easily recognized by distinctive curves of positive unit slope, hump and negative unit slope in the derivative plot. For the observation well of OW-2 at which wellbore storage and transition lasted over an hour, conventional aquifer analysis would suffer from the uniqueness problems and in further result in erroneous hydraulic parameters. Derivative analysis was found to be effective for distinguishing the drawdown data directly reflecting the aquifer property from those reflecting non aquifer effects such as wellbore storage and transition, which offers a unified methodology to yield correct hydraulic parameters from aquifer test data.